State Key Laboratory of Medicinal Chemical Biology, Frontiers Science Center for Cell Responses, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Tianjin 300353, China.
Cloudphar Pharmaceuticals Co., Ltd., Shenzhen, China.
Oxid Med Cell Longev. 2022 Jul 20;2022:1718353. doi: 10.1155/2022/1718353. eCollection 2022.
Diabetic retinopathy (DR), a diabetic microangiopathy caused by diabetes, affects approximately 93 million people, worldwide. However, the drugs used to treat DR have limited efficacy and the variety of side effects. This is possibly because the complicated pathogenesis of DR is associated with multiple proteins. In this work, we attempted to identify potential drugs against DR-associated proteins and predict potential targets for drugs using in silico prediction of chemical-protein interactions (CPI) based on multitarget quantitative structure-activity relationship (mt-QSAR) method. Therefore, we developed 128 binary classifiers to predict the CPI for 15 DR targets using random forest (RF), -nearest neighbours (KNN), support vector machine (SVM), and neural network (NN) algorithms with MACCS, extended connectivity fingerprints (ECFP6) fingerprints, and protein descriptors. In order to facilitate discovery of the novel drugs and target identification using the 128 binary classifiers, a free web server (DRDB) was developed. Compound Danshen Dripping Pills (CDDP), composed of Salvia miltiorrhiza, Panax notoginseng, and borneol, is commonly used in the treatment of cardiovascular diseases. To explore the applicability of DRDB, the potential CPIs of CDDP in treatment of DR were investigated based on DRDB. In vitro experimental validation demonstrated that cryptotanshinone and protocatechuic acid, two key components of CDDP, are capable of targeting ICAM-1 which is one of the key target of DR. We hope that this work can facilitate development of more effective clinical strategies for the treatment of DR.
糖尿病性视网膜病变（DR）是一种由糖尿病引起的糖尿病微血管病变，影响着全球约 9300 万人。然而，用于治疗 DR 的药物疗效有限，且副作用种类繁多。这可能是因为 DR 的复杂发病机制与多种蛋白质有关。在这项工作中，我们试图通过基于多靶定量构效关系（mt-QSAR）方法的化学-蛋白质相互作用（CPI）的计算机预测，来识别针对 DR 相关蛋白的潜在药物，并预测药物的潜在靶点。因此，我们使用随机森林（RF）、-最近邻（KNN）、支持向量机（SVM）和神经网络（NN）算法，结合 MACCS、扩展连接指纹（ECFP6）指纹和蛋白质描述符，开发了 128 个二进制分类器，用于预测 15 个 DR 靶点的 CPI。为了方便使用 128 个二进制分类器发现新的药物和识别新的靶点，开发了一个免费的网络服务器（DRDB）。复方丹参滴丸（CDDP）由丹参、三七和冰片组成，常用于治疗心血管疾病。为了探索 DRDB 的适用性，根据 DRDB 研究了 CDDP 治疗 DR 的潜在 CPIs。体外实验验证表明，CDDP 的两个关键成分隐丹参酮和原儿茶酸能够靶向 DR 的关键靶点之一 ICAM-1。我们希望这项工作能够促进开发更有效的临床策略来治疗 DR。
